Customer-obsessed science
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July 30, 20268 min readInstead of compromising among parameter updates dictated by different training objectives, ControlG allocates computational capacity to objectives sequentially and dynamically.
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July 9, 202610 min read
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Featured news
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CGO 20212021Because of the increasing demand for intensive computation in deep neural networks, researchers have developed both hardware and software mechanisms to reduce the compute and memory burden. A widely adopted approach is to use mixed precision data types. However, it is hard to benefit from mixed precision without hardware specialization because of the overhead of data casting. Recently, hardware vendors
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EACL 20212021Dialog State Tracking (DST), an integral part of modern dialog systems, aims to track user preferences and constraints (slots) in ask oriented dialogs. In real-world settings with constantly changing services, DST systems must generalize to new domains and unseen slot types. Existing methods for DST do not generalize well to new slot names and many require known ontologies of slot types and values for inference
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AAAI 2021 Workshop on DSTC92021Most prior work on task-oriented dialogue systems are restricted to a limited coverage of domain APIs, while users oftentimes have domain related requests that are not covered by the APIs. This challenge track aims to expand the coverage of task-oriented dialogue systems by incorporating external unstructured knowledge sources. We define three tasks: knowledge-seeking turn detection, knowledge selection
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Embedded World Exhibition & Conference 20212021FreeRTOS is a real-time kernel and set of libraries for Internet of Things (IoT) applications. The FreeRTOS kernel provides a portable abstraction layer, task scheduling and interprocess communication (IPC) mechanisms. The main IPC mechanism in FreeRTOS is a concurrent queue: a circular buffer data structure that tasks and interrupt service routines use to exchange messages. As a fundamental building block
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ICML 20212021Despite the recent success of graph neural networks (GNN), common architectures often exhibit significant limitations, including sensitivity to over-smoothing, long-range dependencies, and spurious edges, e.g., as can occur as a result of graph heterophily or adversarial attacks. To at least partially address these issues within a simple transparent framework, we consider a new family of GNN layers designed
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